Construction of Adaptability Evaluation Indicator System for Artificial Intelligence Generated Digital Educational Resources
With the development of Generative Artificial Intelligence(GAI)technology,Artificial Intelligence Generated Content(AIGC)has become a mainstream content generation model after User-Generated Content(UGC)and Professional-Generated Content(PGC),leading to the emergence of Artificial Intelligence Generated Digital Education Resources(AIGDER).However,based on litera-ture review,it is urgent to evaluate AIGDER to improve its adaptability,but there is limited research on this topic.Firstly,an AIGDER a-daptability evaluation model consisting of four elements:credibility of content quality,support for the learning process,conformity with re-source specifications,and satisfaction of teachers and students was constructed through literature review and theoretical analysis of adaptabil-ity.Secondly,an initial evaluation index system was built based on the evaluation model,and revised using the Delphi method,resulting in an AIGDER adaptability evaluation index system consisting of four primary indicators and nineteen secondary indicators.The weighting of this index system was assigned using the analytic hierarchy process(AHP)and a rationality analysis was conducted.Finally,the evaluation index system was tested in a small-scale trial,which verified its scientificity and effectiveness.This indicates that the evaluation index sys-tem can serve as a reference tool for evaluating the adaptability of AIGDER.
GAIAIGCArtificial Intelligence Generated Digital Educational ResourcesAIGDERAdaptability EvaluationIndica-tor System
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